I am a PhD student in the Computer Science Department at the University of Maryland, College Park. I am working with Prof. Yiannis Aloimonos and Cornelia Fermüller at Perception and Robotics Group. I also work closely with Prof. Christopher Metzler. During my master’s study I worked with Prof. Pratap Tokekar on using Reinforcement Learning in Multi-agent System research.
My research focuses on Video/Image Generative Models, neural representations for motion in videos, and 3D vision for robotics.
In this work, we uncover a new perspective on video generation: the first frame acts as a conceptual memory buffer. Leveraging this insight, we achieve robust and generalized video content customization with just 20–50 training examples.
In this work, we adapt pre-trained video diffusion models trained on internet-scale datasets to solve the specialized real-world video task of event-based video interpolation.
ConVRT is an efficient INR framework for video-based turbulence mitigation that operates in test-time optimization manner
We leverage radiance fields to imagine different human views to find the best drone pose for aerial cinematography.
Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture.
CodedEvents is a novel method for optimal point-spread-function engineering for 3D-tracking with event cameras.
We present a self-supervised occupancy prediction technique, ProxMaP, to predict the occupancy within the proximity of the robot to enable faster navigation.
We present a Multi-Agent Reinforcement Learning (MARL) algorithm for the Visibility-based Persistent Monitoring (VPM) problem.
Template from Keunhong Park